Multidimensional procedure for mapping and monitoring urban energy vulnerability at regional level using public data: Proposal and implementation into a case study in Spain

Energy poverty is nowadays one of the biggest challenges to be tackled in the European Union, so identifying the number of households in a situation of energy vulnerability and taking the necessary measures to protect vulnerable and energy poor customers is considered to be essential. In this study, a simple methodology for identifying and monitoring energy vulnerable areas based on information available in public databases is pre-sented. This paper brings to light the potential of existing public data for evaluating energy vulnerability, and the nature of these data also enables the evolution of vulnerability levels and the effect of potential measures implemented to be evaluated. The proposed method allows energy vulnerability to be mapped and diagnosed, at census section level, by means of a three-dimensional index that takes into account building features and energy expenses and two socio-economic indicators, giving rise to a vulnerability traffic-light. The method is then illustrated with the evaluation of the energy vulnerability of a region located in northern Spain (Greater Bilbao), where 13% of the census sections or 93,000 inhabitants reside (11% of the total population analysed), have been identified as suffering different levels of energy vulnerability. A geographical pattern has also been clearly recognised.

Automated summary

This study presents a simple methodology for identifying and monitoring energy vulnerable areas based on publicly available information. The proposed method maps and diagnoses energy vulnerability at the census section level by considering building features, energy expenses, and socio-economic indicators. The vulnerability of the Greater Bilbao region in northern Spain was evaluated using this method, revealing that 13% of the population in this area resides in different levels of energy vulnerability. A clear geographical pattern was also identified.